High-resolution astronomical images can be reconstructed
from several blurred and noisy low-resolution images using a computational
process known as superresolution reconstruction. Superresolution
reconstruction is closely related to image deconvolution, except that the
low-resolution images are not registered and their relative translations
and rotations must be estimated in the process. The novelty of our approach
to the superresolution problem is the use of wavelets and related
multiresolution methods within an expectation-maximization reconstruction
process to improve the accuracy and visual quality of the reconstructed
image. Simulations demonstrate the effectiveness of the proposed
method, including its ability to distinguish between tightly grouped stars
with a small set of observations.